{"id":"W2118670046","doi":"10.1002/sim.2518","title":"Confidence intervals for multinomial logistic regression in sparse data","year":2006,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":54,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lunenfeld-Tanenbaum Research Institute; University of Toronto; Mount Sinai Hospital","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Multinomial logistic regression; Statistics; Logistic regression; Mathematics; Covariate; Confidence interval; Likelihood function; Binary data; Multinomial distribution; Econometrics; Wald test; Maximum likelihood; Binary number; Statistical hypothesis testing","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.001932848,0.0002028435,0.0005880645,0.0001576188,0.00003823682,0.00001598877,0.0004792435,0.00009677425,0.0002422162],"category_scores_gemma":[0.02711602,0.0001532367,0.00001335249,0.0001851684,0.0003590012,0.00005764549,0.0001392526,0.0002639425,0.000006227527],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000716813,"about_ca_system_score_gemma":0.00006374375,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009634527,"about_ca_topic_score_gemma":0.001551038,"domain_scores_codex":[0.9977827,0.0001990396,0.0009110778,0.000457858,0.0002856817,0.0003636077],"domain_scores_gemma":[0.989191,0.009787946,0.0002221432,0.0006244587,0.0001070206,0.00006741202],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000104866,0.0001155295,0.001531588,0.0002958783,0.000004186888,0.0001181246,0.0001381207,0.000002307386,0.0001901719,0.9412246,0.03565139,0.02062321],"study_design_scores_gemma":[0.001749026,0.0001582504,0.005202849,0.0009250068,0.00003368085,0.000006730391,0.0001044554,0.03217084,0.00004643983,0.9589484,0.0004825929,0.0001717695],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0004609532,0.00009413523,0.9960472,0.0003348191,0.0004224965,0.0005113427,0.001052251,0.00002320184,0.001053579],"genre_scores_gemma":[0.09904484,0.00002846855,0.9000895,0.00009599074,0.0002743137,0.00004020355,0.000249213,0.0000227593,0.0001547393],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.09858388,"threshold_uncertainty_score":0.981079,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2664078337446776,"score_gpt":0.4945271978202856,"score_spread":0.228119364075608,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}